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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2024-08-29 09:41:09
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Restricted Document
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15111
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UniSZA
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| internalnotes |
Cichocki, A., Zdunek, R., Phan, A., & Amari, S. (2009). Nonnegative matrix and tensor factorizations: Applications to exploratory multi-way data analysis and blind source separation. New York, NY: Wiley. http://dx.doi.org/10.1002/9780470747278 Cheng, M., Gao, X., Gao, S., & Xu, D. (2002). Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans. Biomed. Eng., 49, 1181-1186. http://dx.doi.org/10.1109/TBME.2002.803536 EEGLAB News. (2013). Retrieded from http://sccn.ucsd.edu/eeglab/ Hakvoort, G., Reuderink, B., & Obbink, M. (2011). Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system. Technical Report, TR-CTIT-11-03, EEMCS. ISSN:1381-3625. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321-377. Hovagim, B., Toshihisa, T., & Andrzej, C. (2010). Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface. Neuroscience Letters, 469, 34-38. http://dx.doi.org/10.1016/j.neulet.2009.11.039 Kim, T. K., & Cipolla, R. (2009). Canonical correlation analysis of video volume tensor for action categorization and detection. IEEE Trans. PAMI., 31, 1415-1428. http://dx.doi.org/10.1109/TPAMI.2008.167 Lai, P. L., & Fyfe, C. (1999). A neural implementation of canonical correlation analysis. Neural Networks, 12, 1391-1397. http://dx.doi.org/10.1016/S0893-6080(99)00075-1 Lin Z., Zhang, C., Wu, W., & Gao, X. (2006). Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng., 53, 2610-2614. http://dx.doi.org/10.1109/TBME.2006.886577 MÄuller-Putz, G. R., Scherer, R., Brauneis, C., & Pfurtscheller, G. (2005). Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural. Eng., 2, 123-130. http://dx.doi.org/10.1088/1741-2560/2/4/008 Müller, M. M., & Hillyard, S. (2000). Concurrent recording of steady-state and transient event related potentials as indices of visual-spatial selective attention. Clin. Neurophysiol., 111, 1544-1552. http://dx.doi.org/10.1016/S1388-2457(00)00371-0 Oja, E. A. (1982). Simplified neuron model as a principle component analyzer. Journal of Mathematical Biology, 16, 267-273. http://dx.doi.org/10.1007/BF00275687 Regan, D. (1977). Steady-state evoked potentials. J. Opt. Soc. Am., 67, 1475-1489. http://dx.doi.org/10.1364/JOSA.67.001475 Sanger, T. (1990). Analysis of the two-dimensional receptive fields learned by the generalized hebbian algorithm in response to random dot input. Biological Cybernetics, 63, 221-228. http://dx.doi.org/10.1007/BF00195861 SSVEP DATA. (2013). Retrieved from http://www.bakardjian.com/work/ssvep_data_Bakardjian.html/ Zhang, Y., Jin, J., Qing, X., Wang, B., & Wang, X. (2011). LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomedical Signal Processing and Control, 7(2), 104-111. http://dx.doi.org/10.1016/j.bspc.2011.02.002 Zhang, Y., Zhou, G., Zhao, Q., Onishi, A., Wang, J. J., & Cichocki, A. (2011). Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-based BCIs. In Neural Information Processing (pp. 287-295). Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-24955-6_35 Zhu, D. H., Bieger, J., Molina, G. G., &Aarts, R. (2010). A survey of stimulation methods used in SSVEP-based BCIs. Computational intelligence and neuroscience, 2010, 1. http://dx.doi.org/10.1155/2010/702357 Zhu, D., Molina, G. G., Mihajlovic, V., & Aarts, R. M. (2010). Phase synchrony analysis for SSVEP-based BCIs. 2nd International Conference on Computer Engineering and Technology (ICCET 2010), April 16-18, China.
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4925-01-FH02-FRTK-14-00379.pdf
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administrator
Administrator
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15111
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15111 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15111 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 13 1.6 administrator Administrator 2024-08-29 09:41:09 4925-01-FH02-FRTK-14-00379.pdf UniSZA Private Access Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis Modern Applied Science This paper presents an algorithm for extracting underlying frequency components of massive Electroencephalogram (EEG) data. Frequency components of these data play a vital role to realize brain-body condition. Usually, a huge amount of time and specially built computers are essential to process these EEG data having different subjects. It also restricts to visualize inherent frequency of EEG for a general practitioner. An algorithm is developed using two-stage cascaded architecture of canonical correlation analysis with neural network named multiway neural canonical correlation analysis (MNCCA) to address three major challenges for extracting frequency components from EEG data, such as: (a) It processes multiway data which are feed sequentially into neural network, rather than feeding whole data at a time, (b) It uses the conventional personal computer instead of special computer built for such application, (c) It spends very short time for a moderate data set consisting of several ways (time, trials and channels). The experimental results are obtained with three different kinds of networks having linear, nonlinear and nonlinear feedback structures. The inherent dominant frequency of 1 Hz having a quite resemblance with EEG landscape has been found. This provides a great opportunity in analyzing brain-body function. 8 1 Canadian Center of Science and Education Canadian Center of Science and Education 164-175 Cichocki, A., Zdunek, R., Phan, A., & Amari, S. (2009). Nonnegative matrix and tensor factorizations: Applications to exploratory multi-way data analysis and blind source separation. New York, NY: Wiley. http://dx.doi.org/10.1002/9780470747278 Cheng, M., Gao, X., Gao, S., & Xu, D. (2002). Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans. Biomed. Eng., 49, 1181-1186. http://dx.doi.org/10.1109/TBME.2002.803536 EEGLAB News. (2013). Retrieded from http://sccn.ucsd.edu/eeglab/ Hakvoort, G., Reuderink, B., & Obbink, M. (2011). Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system. Technical Report, TR-CTIT-11-03, EEMCS. ISSN:1381-3625. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321-377. Hovagim, B., Toshihisa, T., & Andrzej, C. (2010). Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface. Neuroscience Letters, 469, 34-38. http://dx.doi.org/10.1016/j.neulet.2009.11.039 Kim, T. K., & Cipolla, R. (2009). Canonical correlation analysis of video volume tensor for action categorization and detection. IEEE Trans. PAMI., 31, 1415-1428. http://dx.doi.org/10.1109/TPAMI.2008.167 Lai, P. L., & Fyfe, C. (1999). A neural implementation of canonical correlation analysis. Neural Networks, 12, 1391-1397. http://dx.doi.org/10.1016/S0893-6080(99)00075-1 Lin Z., Zhang, C., Wu, W., & Gao, X. (2006). Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng., 53, 2610-2614. http://dx.doi.org/10.1109/TBME.2006.886577 MÄuller-Putz, G. R., Scherer, R., Brauneis, C., & Pfurtscheller, G. (2005). Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural. Eng., 2, 123-130. http://dx.doi.org/10.1088/1741-2560/2/4/008 Müller, M. M., & Hillyard, S. (2000). Concurrent recording of steady-state and transient event related potentials as indices of visual-spatial selective attention. Clin. Neurophysiol., 111, 1544-1552. http://dx.doi.org/10.1016/S1388-2457(00)00371-0 Oja, E. A. (1982). Simplified neuron model as a principle component analyzer. Journal of Mathematical Biology, 16, 267-273. http://dx.doi.org/10.1007/BF00275687 Regan, D. (1977). Steady-state evoked potentials. J. Opt. Soc. Am., 67, 1475-1489. http://dx.doi.org/10.1364/JOSA.67.001475 Sanger, T. (1990). Analysis of the two-dimensional receptive fields learned by the generalized hebbian algorithm in response to random dot input. Biological Cybernetics, 63, 221-228. http://dx.doi.org/10.1007/BF00195861 SSVEP DATA. (2013). Retrieved from http://www.bakardjian.com/work/ssvep_data_Bakardjian.html/ Zhang, Y., Jin, J., Qing, X., Wang, B., & Wang, X. (2011). LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomedical Signal Processing and Control, 7(2), 104-111. http://dx.doi.org/10.1016/j.bspc.2011.02.002 Zhang, Y., Zhou, G., Zhao, Q., Onishi, A., Wang, J. J., & Cichocki, A. (2011). Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-based BCIs. In Neural Information Processing (pp. 287-295). Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-24955-6_35 Zhu, D. H., Bieger, J., Molina, G. G., &Aarts, R. (2010). A survey of stimulation methods used in SSVEP-based BCIs. Computational intelligence and neuroscience, 2010, 1. http://dx.doi.org/10.1155/2010/702357 Zhu, D., Molina, G. G., Mihajlovic, V., & Aarts, R. M. (2010). Phase synchrony analysis for SSVEP-based BCIs. 2nd International Conference on Computer Engineering and Technology (ICCET 2010), April 16-18, China.
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| spellingShingle |
Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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| summary |
This paper presents an algorithm for extracting underlying frequency components of massive Electroencephalogram (EEG) data. Frequency components of these data play a vital role to realize brain-body condition. Usually, a huge amount of time and specially built computers are essential to process these EEG data having different subjects. It also restricts to visualize inherent frequency of EEG for a general practitioner. An algorithm is developed using two-stage cascaded architecture of canonical correlation analysis with neural network named multiway neural canonical correlation analysis (MNCCA) to address three major challenges for extracting frequency components from EEG data, such as: (a) It processes multiway data which are feed sequentially into neural network, rather than feeding whole data at a time, (b) It uses the conventional personal computer instead of special computer built for such application, (c) It spends very short time for a moderate data set consisting of several ways (time, trials and channels). The experimental results are obtained with three different kinds of networks having linear, nonlinear and nonlinear feedback structures. The inherent dominant frequency of 1 Hz having a quite resemblance with EEG landscape has been found. This provides a great opportunity in analyzing brain-body function.
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| title |
Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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| title_full |
Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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| title_fullStr |
Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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| title_full_unstemmed |
Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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| title_short |
Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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| title_sort |
extraction of inherent frequency components of multiway eeg data using two-stage neural canonical correlation analysis
|